Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/1523
Title: Determining the shear strength of FRP-RC beams using soft computing and code methods
Authors: Yavuz, Günnur
Keywords: Internal Frp Bar
Reinforced Concrete
Beam
Shear Strength
Artificial Neural Network
Reinforced Concrete Beams
Prediction
Capacity
Polymer
Issue Date: 2019
Publisher: TECHNO-PRESS
Abstract: In recent years, multiple experimental studies have been performed on using fiber reinforced polymer (FRP) bars in reinforced concrete (RC) structural members. FRP bars provide a new type of reinforcement that avoids the corrosion of traditional steel reinforcement. In this study, predicting the shear strength of RC beams with FRP longitudinal bars using artificial neural networks (ANNs) is investigated as a different approach from the current specific codes. An ANN model was developed using the experimental data of 104 FRP-RC specimens from an existing database in the literature. Seven different input parameters affecting the shear strength of FRP bar reinforced RC beams were selected to create the ANN structure. The most convenient ANN algorithm was determined as traingdx. The results from current codes (ACI440.1R-15 and JSCE) and existing literature in predicting the shear strength of FRP-RC beams were investigated using the identical test data. The study shows that the ANN model produces acceptable predictions for the ultimate shear strength of FRP-RC beams (maximum R-2 approximate to 0.97). Additionally, the ANN model provides more accurate predictions for the shear capacity than the other computed methods in the ACI440.1R-15, JSCE codes and existing literature for considering different performance parameters.
URI: https://doi.org/10.12989/cac.2019.23.1.049
https://hdl.handle.net/20.500.13091/1523
ISSN: 1598-8198
1598-818X
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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